Randomized trial of calcipotriol combined with 5-fluorouracil for skin cancer precursor immunotherapy

BACKGROUND. Actinic keratosis is a precursor to cutaneous squamous cell carcinoma. Long treatment durations and severe side effects have limited the efficacy of current actinic keratosis treatments. Thymic stromal lymphopoietin (TSLP) is an epithelium-derived cytokine that induces a robust antitumor immunity in barrier-defective skin. Here, we investigated the efficacy of calcipotriol, a topical TSLP inducer, in combination with 5-fluorouracil (5-FU) as an immunotherapy for actinic keratosis. METHODS. The mechanism of calcipotriol action against skin carcinogenesis was examined in genetically engineered mouse models. The efficacy and safety of 0.005% calcipotriol ointment combined with 5% 5-FU cream were compared with Vaseline plus 5-FU for the field treatment of actinic keratosis in a randomized, double-blind clinical trial involving 131 participants. The assigned treatment was self-applied to the entirety of the qualified anatomical sites (face, scalp, and upper extremities) twice daily for 4 consecutive days. The percentage of reduction in the number of actinic keratoses (primary outcome), local skin reactions, and immune activation parameters were assessed. RESULTS. Calcipotriol suppressed skin cancer development in mice in a TSLP-dependent manner. Four-day application of calcipotriol plus 5-FU versus Vaseline plus 5-FU led to an 87.8% versus 26.3% mean reduction in the number of actinic keratoses in participants (P < 0.0001). Importantly, calcipotriol plus 5-FU treatment induced TSLP, HLA class II, and natural killer cell group 2D (NKG2D) ligand expression in the lesional keratinocytes associated with a marked CD4(+) T cell infiltration, which peaked on days 10–11 after treatment, without pain, crusting, or ulceration. CONCLUSION. Our findings demonstrate the synergistic effects of calcipotriol and 5-FU treatment in optimally activating a CD4(+) T cell–mediated immunity against actinic keratoses and, potentially, cancers of the skin and other organs. TRIAL REGISTRATION. ClinicalTrials.gov NCT02019355. FUNDING. Not applicable (investigator-initiated clinical trial)

COMPUTATIONAL TECHNIQUES FOR SKIN LESION TRACKING AND CLASSIFICATION

We propose image-based automatic pigmented skin lesion (PSL) tracking and classification systems for early skin cancer detection. The input to our PSL tracking system is a pair of skin back images of the same subject. The output is the correspondence (matching) between the detected lesions and the identification of newly appearing (or disappearing) ones. We start by automatically detecting a set of anatomical landmarks by globally optimizing a pictorial structure. The detected landmarks are used to restrict the search space during lesion localization and encode the anatomical spatial context of lesions using a set of Jacobian based features, which are useful for lesion matching. The matching step is performed by an uncertainty-based feature learning approach using a high order Markov Random Field (MRF) optimization framework. The LND detection and PSL matching steps involve the optimization of energy functions with hyper-parameters, which are learned using a structured support vector machine. Given the dependence of the lesion matching on the detected landmarks, we propose an adaptive system that predicts the landmark detection error and leverages it to automatically adapt the lesion matching objective function. We also make the following contributions in our PSL classification system. In our work, we focus on extracting features for streak detection due to the clinical importance of the absence or presence of the streaks in dermoscopic images. To this end, we develop a novel hair disocclusion method using dual-channel quaternion tubularness filters and MRF-based multi-label optimization. To facilitate a comprehensive evaluation on hair segmentation, we provide a publicly available new hair simulator software. Further, integrating the quaternion tubularness filters and context of the eigenvectors, we propose a novel lesion descriptor. At the end, we apply weakly supervised learning approaches to perform the PSL classification task